sn-ENS neuron integration
for all current Baf53cre Ahr CTL/CKO data
#### sample info
## processed obj
##
# CR7d CTL x4 CKO x3 (x1 CKO failed)
# 20231220
#
# Steady state CTL x3 CKO x3
# 20240318
#
#
##source("/Shared_win/projects/RNA_normal/analysis.10x.r")load processed obj
merged.list <- list(CR7d.CTL_CKO=readRDS("I:/Shared_win/projects/20231220_10x_SZJ/analysis_plus/GEX0131.seur.Anno2.rds"),
Stst.CTL_CKO=readRDS("I:/Shared_win/projects/20240318_10x_SZJ/analysis_plus/GEX0430.seur.pure_Anno.rds")
)
merged.list## $CR7d.CTL_CKO
## An object of class Seurat
## 24081 features across 16520 samples within 1 assay
## Active assay: RNA (24081 features, 1046 variable features)
## 3 dimensional reductions calculated: pca, tsne, umap
##
## $Stst.CTL_CKO
## An object of class Seurat
## 22951 features across 11932 samples within 1 assay
## Active assay: RNA (22951 features, 1027 variable features)
## 3 dimensional reductions calculated: pca, tsne, umaplapply(merged.list, function(x){unique(x$FB.info)})## $CR7d.CTL_CKO
## [1] CTL.3 CTL.4 CTL.1 CKO.4 CKO.3 CKO.1 CTL.2
## Levels: CTL.1 CTL.2 CTL.3 CTL.4 CKO.1 CKO.3 CKO.4
##
## $Stst.CTL_CKO
## [1] CTL.3 CKO.3 CTL.1 CTL.2 CKO.1 CKO.2
## Levels: CTL.1 CTL.2 CTL.3 CKO.1 CKO.2 CKO.3lapply(merged.list, function(x){unique(x$cnt)})## $CR7d.CTL_CKO
## [1] CTL CKO
## Levels: CTL CKO
##
## $Stst.CTL_CKO
## [1] CTL CKO
## Levels: CTL CKOlapply(merged.list, function(x){unique(x$rep)})## $CR7d.CTL_CKO
## [1] "rep.3" "rep.4" "rep.1" "rep.2"
##
## $Stst.CTL_CKO
## [1] "rep.3" "rep.1" "rep.2"modify FB.info
rebuild cnt and rep as:
CR7d.CTL
CR7d.CKO
Stst.CTL
Stst.CKO
CR7d.CTL_CKO
#
merged.list$CR7d.CTL_CKO$sample <- as.character(merged.list$CR7d.CTL_CKO$FB.info)
merged.list$CR7d.CTL_CKO$sample <- as.vector(unlist(sapply(merged.list$CR7d.CTL_CKO$sample, function(x){
gsub("CTL.","CR7d.CTL",x)
})))
merged.list$CR7d.CTL_CKO$sample <- as.vector(unlist(sapply(merged.list$CR7d.CTL_CKO$sample, function(x){
gsub("CKO.","CR7d.CKO",x)
})))
merged.list$CR7d.CTL_CKO$cnt <- gsub("1$|2$|3$|4$","",as.character(merged.list$CR7d.CTL_CKO$sample))
merged.list$CR7d.CTL_CKO$sample[merged.list$CR7d.CTL_CKO$sample %in% "CR7d.CKO3"] <- "CR7d.CKO2"
merged.list$CR7d.CTL_CKO$sample[merged.list$CR7d.CTL_CKO$sample %in% "CR7d.CKO4"] <- "CR7d.CKO3"
merged.list$CR7d.CTL_CKO$rep <- paste0("rep",
gsub("CR7d.CTL|CR7d.CKO","",as.character(merged.list$CR7d.CTL_CKO$sample)))
merged.list$CR7d.CTL_CKO$tissue <- "Ileum"head(merged.list$CR7d.CTL_CKO$FB.info,10)## AAACCCAAGAAGTCTA-1 AAACCCAAGACATACA-1 AAACCCAAGACTCATC-1 AAACCCAAGAGGTTTA-1
## CTL.3 CTL.3 CTL.4 CTL.1
## AAACCCAAGGCTCTAT-1 AAACCCAAGGTCTTTG-1 AAACCCACAGATAAAC-1 AAACCCAGTATGAGAT-1
## CKO.4 CKO.3 CTL.4 CKO.1
## AAACCCAGTATGGAAT-1 AAACCCAGTATGGGAC-1
## CTL.4 CTL.2
## Levels: CTL.1 CTL.2 CTL.3 CTL.4 CKO.1 CKO.3 CKO.4head(merged.list$CR7d.CTL_CKO$sample,10)## AAACCCAAGAAGTCTA-1 AAACCCAAGACATACA-1 AAACCCAAGACTCATC-1 AAACCCAAGAGGTTTA-1
## "CR7d.CTL3" "CR7d.CTL3" "CR7d.CTL4" "CR7d.CTL1"
## AAACCCAAGGCTCTAT-1 AAACCCAAGGTCTTTG-1 AAACCCACAGATAAAC-1 AAACCCAGTATGAGAT-1
## "CR7d.CKO3" "CR7d.CKO2" "CR7d.CTL4" "CR7d.CKO1"
## AAACCCAGTATGGAAT-1 AAACCCAGTATGGGAC-1
## "CR7d.CTL4" "CR7d.CTL2"head(merged.list$CR7d.CTL_CKO$cnt,10)## AAACCCAAGAAGTCTA-1 AAACCCAAGACATACA-1 AAACCCAAGACTCATC-1 AAACCCAAGAGGTTTA-1
## "CR7d.CTL" "CR7d.CTL" "CR7d.CTL" "CR7d.CTL"
## AAACCCAAGGCTCTAT-1 AAACCCAAGGTCTTTG-1 AAACCCACAGATAAAC-1 AAACCCAGTATGAGAT-1
## "CR7d.CKO" "CR7d.CKO" "CR7d.CTL" "CR7d.CKO"
## AAACCCAGTATGGAAT-1 AAACCCAGTATGGGAC-1
## "CR7d.CTL" "CR7d.CTL"head(merged.list$CR7d.CTL_CKO$rep,10)## AAACCCAAGAAGTCTA-1 AAACCCAAGACATACA-1 AAACCCAAGACTCATC-1 AAACCCAAGAGGTTTA-1
## "rep3" "rep3" "rep4" "rep1"
## AAACCCAAGGCTCTAT-1 AAACCCAAGGTCTTTG-1 AAACCCACAGATAAAC-1 AAACCCAGTATGAGAT-1
## "rep3" "rep2" "rep4" "rep1"
## AAACCCAGTATGGAAT-1 AAACCCAGTATGGGAC-1
## "rep4" "rep2"head(merged.list$CR7d.CTL_CKO$tissue,10)## AAACCCAAGAAGTCTA-1 AAACCCAAGACATACA-1 AAACCCAAGACTCATC-1 AAACCCAAGAGGTTTA-1
## "Ileum" "Ileum" "Ileum" "Ileum"
## AAACCCAAGGCTCTAT-1 AAACCCAAGGTCTTTG-1 AAACCCACAGATAAAC-1 AAACCCAGTATGAGAT-1
## "Ileum" "Ileum" "Ileum" "Ileum"
## AAACCCAGTATGGAAT-1 AAACCCAGTATGGGAC-1
## "Ileum" "Ileum"Stst.CTL_CKO
#
merged.list$Stst.CTL_CKO$sample <- as.character(merged.list$Stst.CTL_CKO$FB.info)
merged.list$Stst.CTL_CKO$sample <- as.vector(unlist(sapply(merged.list$Stst.CTL_CKO$sample, function(x){
gsub("CTL.","Stst.CTL",x)
})))
merged.list$Stst.CTL_CKO$sample <- as.vector(unlist(sapply(merged.list$Stst.CTL_CKO$sample, function(x){
gsub("CKO.","Stst.CKO",x)
})))
merged.list$Stst.CTL_CKO$cnt <- gsub("1$|2$|3$|4$","",as.character(merged.list$Stst.CTL_CKO$sample))
merged.list$Stst.CTL_CKO$rep <- paste0("rep",
gsub("Stst.CTL|Stst.CKO","",as.character(merged.list$Stst.CTL_CKO$sample)))
merged.list$Stst.CTL_CKO$tissue <- "Ileum"head(merged.list$Stst.CTL_CKO$FB.info,10)## AAACCCAAGAATACAC-1 AAACCCAAGCAATAGT-1 AAACCCAAGGTGAGCT-1 AAACCCAAGTGGACGT-1
## CTL.3 CKO.3 CTL.1 CKO.3
## AAACCCACAACGAGGT-1 AAACCCACAAGAGTAT-1 AAACCCACAATCAAGA-1 AAACCCACAGAGTCAG-1
## CTL.1 CKO.3 CTL.2 CKO.3
## AAACCCAGTATCGATC-1 AAACCCATCAAGTTGC-1
## CTL.2 CKO.1
## Levels: CTL.1 CTL.2 CTL.3 CKO.1 CKO.2 CKO.3head(merged.list$Stst.CTL_CKO$sample,10)## AAACCCAAGAATACAC-1 AAACCCAAGCAATAGT-1 AAACCCAAGGTGAGCT-1 AAACCCAAGTGGACGT-1
## "Stst.CTL3" "Stst.CKO3" "Stst.CTL1" "Stst.CKO3"
## AAACCCACAACGAGGT-1 AAACCCACAAGAGTAT-1 AAACCCACAATCAAGA-1 AAACCCACAGAGTCAG-1
## "Stst.CTL1" "Stst.CKO3" "Stst.CTL2" "Stst.CKO3"
## AAACCCAGTATCGATC-1 AAACCCATCAAGTTGC-1
## "Stst.CTL2" "Stst.CKO1"head(merged.list$Stst.CTL_CKO$cnt,10)## AAACCCAAGAATACAC-1 AAACCCAAGCAATAGT-1 AAACCCAAGGTGAGCT-1 AAACCCAAGTGGACGT-1
## "Stst.CTL" "Stst.CKO" "Stst.CTL" "Stst.CKO"
## AAACCCACAACGAGGT-1 AAACCCACAAGAGTAT-1 AAACCCACAATCAAGA-1 AAACCCACAGAGTCAG-1
## "Stst.CTL" "Stst.CKO" "Stst.CTL" "Stst.CKO"
## AAACCCAGTATCGATC-1 AAACCCATCAAGTTGC-1
## "Stst.CTL" "Stst.CKO"head(merged.list$Stst.CTL_CKO$rep,10)## AAACCCAAGAATACAC-1 AAACCCAAGCAATAGT-1 AAACCCAAGGTGAGCT-1 AAACCCAAGTGGACGT-1
## "rep3" "rep3" "rep1" "rep3"
## AAACCCACAACGAGGT-1 AAACCCACAAGAGTAT-1 AAACCCACAATCAAGA-1 AAACCCACAGAGTCAG-1
## "rep1" "rep3" "rep2" "rep3"
## AAACCCAGTATCGATC-1 AAACCCATCAAGTTGC-1
## "rep2" "rep1"head(merged.list$Stst.CTL_CKO$tissue,10)## AAACCCAAGAATACAC-1 AAACCCAAGCAATAGT-1 AAACCCAAGGTGAGCT-1 AAACCCAAGTGGACGT-1
## "Ileum" "Ileum" "Ileum" "Ileum"
## AAACCCACAACGAGGT-1 AAACCCACAAGAGTAT-1 AAACCCACAATCAAGA-1 AAACCCACAGAGTCAG-1
## "Ileum" "Ileum" "Ileum" "Ileum"
## AAACCCAGTATCGATC-1 AAACCCATCAAGTTGC-1
## "Ileum" "Ileum"merge
all.batch.merged <- merge(merged.list[[1]],
merged.list[2:length(merged.list)],
merge.data=T)## Warning in CheckDuplicateCellNames(object.list = objects): Some cell names are
## duplicated across objects provided. Renaming to enforce unique cell names.all.batch.merged## An object of class Seurat
## 24355 features across 28452 samples within 1 assay
## Active assay: RNA (24355 features, 0 variable features)rm(merged.list)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 7790095 416.1 13105874 700.0 13105874 700.0
## Vcells 110003636 839.3 1401836563 10695.2 1710833604 13052.7unique(all.batch.merged$cnt)## [1] "CR7d.CTL" "CR7d.CKO" "Stst.CTL" "Stst.CKO"unique(all.batch.merged$rep)## [1] "rep3" "rep4" "rep1" "rep2"all.batch.merged$cnt <- factor(as.character(all.batch.merged$cnt),
levels = c("Stst.CTL","Stst.CKO",
"CR7d.CTL","CR7d.CKO"))
head(all.batch.merged$cnt)## AAACCCAAGAAGTCTA-1_1 AAACCCAAGACATACA-1_1 AAACCCAAGACTCATC-1_1
## CR7d.CTL CR7d.CTL CR7d.CTL
## AAACCCAAGAGGTTTA-1_1 AAACCCAAGGCTCTAT-1_1 AAACCCAAGGTCTTTG-1_1
## CR7d.CTL CR7d.CKO CR7d.CKO
## Levels: Stst.CTL Stst.CKO CR7d.CTL CR7d.CKO# modify orig.ident
all.batch.merged$orig.ident <- as.character(all.batch.merged$cnt)
all.batch.merged$orig.ident[all.batch.merged$orig.ident %in% c("Stst.CTL","Stst.CKO")] <- "Stst.CTL_CKO"
all.batch.merged$orig.ident[all.batch.merged$orig.ident %in% c("CR7d.CTL","CR7d.CKO")] <- "CR7d.CTL_CKO"
all.batch.merged$orig.ident <- factor(all.batch.merged$orig.ident,
levels = c("Stst.CTL_CKO","CR7d.CTL_CKO"))
head(all.batch.merged$orig.ident)## AAACCCAAGAAGTCTA-1_1 AAACCCAAGACATACA-1_1 AAACCCAAGACTCATC-1_1
## CR7d.CTL_CKO CR7d.CTL_CKO CR7d.CTL_CKO
## AAACCCAAGAGGTTTA-1_1 AAACCCAAGGCTCTAT-1_1 AAACCCAAGGTCTTTG-1_1
## CR7d.CTL_CKO CR7d.CTL_CKO CR7d.CTL_CKO
## Levels: Stst.CTL_CKO CR7d.CTL_CKOtable(all.batch.merged@meta.data[,c("orig.ident","cnt")])## cnt
## orig.ident Stst.CTL Stst.CKO CR7d.CTL CR7d.CKO
## Stst.CTL_CKO 5909 6023 0 0
## CR7d.CTL_CKO 0 0 10280 6240table(all.batch.merged@meta.data[,c("orig.ident","sample")])## sample
## orig.ident CR7d.CKO1 CR7d.CKO2 CR7d.CKO3 CR7d.CTL1 CR7d.CTL2 CR7d.CTL3
## Stst.CTL_CKO 0 0 0 0 0 0
## CR7d.CTL_CKO 2365 1643 2232 2640 2507 2348
## sample
## orig.ident CR7d.CTL4 Stst.CKO1 Stst.CKO2 Stst.CKO3 Stst.CTL1 Stst.CTL2
## Stst.CTL_CKO 0 1998 2218 1807 2122 1714
## CR7d.CTL_CKO 2785 0 0 0 0 0
## sample
## orig.ident Stst.CTL3
## Stst.CTL_CKO 2073
## CR7d.CTL_CKO 0Integration
#Idents(all.batch.merged) <- "cnt"
Idents(all.batch.merged) <- "orig.ident"
head(all.batch.merged@active.ident)## AAACCCAAGAAGTCTA-1_1 AAACCCAAGACATACA-1_1 AAACCCAAGACTCATC-1_1
## CR7d.CTL_CKO CR7d.CTL_CKO CR7d.CTL_CKO
## AAACCCAAGAGGTTTA-1_1 AAACCCAAGGCTCTAT-1_1 AAACCCAAGGTCTTTG-1_1
## CR7d.CTL_CKO CR7d.CTL_CKO CR7d.CTL_CKO
## Levels: Stst.CTL_CKO CR7d.CTL_CKOgc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 7791081 416.1 13105874 700.0 13105874 700.0
## Vcells 110006196 839.3 1121469251 8556.2 1710833604 13052.7all.batch.merged## An object of class Seurat
## 24355 features across 28452 samples within 1 assay
## Active assay: RNA (24355 features, 0 variable features)SCT
batchcor.seu.list <- list()
#for(ttt in levels(all.batch.merged$cnt)){
# ttt.seu <- subset(all.batch.merged, subset=cnt == ttt)
# Idents(ttt.seu) <- "rep"
for(ttt in levels(all.batch.merged$orig.ident)){
ttt.seu <- subset(all.batch.merged, subset=orig.ident == ttt)
Idents(ttt.seu) <- "sample"
ttt.seu <- SCTransform(ttt.seu, variable.features.n = 3000, vst.flavor="v2",
return.only.var.genes = FALSE)
batchcor.seu.list[[ttt]] <- ttt.seu
}## vst.flavor='v2' set, setting model to use fixed slope and exclude poisson genes.## Calculating cell attributes from input UMI matrix: log_umi## Total Step 1 genes: 17656## Total overdispersed genes: 14190## Excluding 3466 genes from Step 1 because they are not overdispersed.## Variance stabilizing transformation of count matrix of size 19662 by 11932## Model formula is y ~ log_umi## Get Negative Binomial regression parameters per gene## Using 2000 genes, 5000 cells##
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|======================================================================| 100%## Calculating gene attributes## Wall clock passed: Time difference of 1.36219 mins## Determine variable features## Place corrected count matrix in counts slot## Centering data matrix## Set default assay to SCT## vst.flavor='v2' set, setting model to use fixed slope and exclude poisson genes.## Calculating cell attributes from input UMI matrix: log_umi## Total Step 1 genes: 18059## Total overdispersed genes: 15302## Excluding 2757 genes from Step 1 because they are not overdispersed.## Variance stabilizing transformation of count matrix of size 20759 by 16520## Model formula is y ~ log_umi## Get Negative Binomial regression parameters per gene## Using 2000 genes, 5000 cells##
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|======================================================================| 100%## Calculating gene attributes## Wall clock passed: Time difference of 1.872609 mins## Determine variable features## Place corrected count matrix in counts slot## Centering data matrix## Set default assay to SCTgc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 8092367 432.2 13105874 700.0 13105874 700.0
## Vcells 877719746 6696.5 2811167344 21447.6 2808264459 21425.4rm(ttt.seu)
rm(all.batch.merged)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 8092284 432.2 13105874 700.0 13105874 700.0
## Vcells 780824647 5957.3 2248933876 17158.1 2808264459 21425.4feature.raw <- SelectIntegrationFeatures(object.list = batchcor.seu.list, nfeatures = 3000)
head(feature.raw,300)## [1] "Cntn5" "Kcnip4" "Mgat4c" "Cntnap2"
## [5] "Zfp804a" "Klhl1" "Kcnb2" "Gal"
## [9] "Nrxn3" "Pcdh9" "Gm30613" "Adgrg6"
## [13] "Lingo2" "Asic2" "Robo2" "Ntng1"
## [17] "Rbfox1" "Nrg1" "Sgcz" "Cpne4"
## [21] "Cdh18" "Cmah" "Gm32647" "Prkg1"
## [25] "Schip1" "Gm29521" "Tmeff2" "Ebf1"
## [29] "Ntrk3" "Gm15261" "Kcnq5" "Cdh9"
## [33] "Brinp3" "Egfem1" "March1" "Ano2"
## [37] "4930509J09Rik" "Cdh8" "Nmu" "Csmd3"
## [41] "Gm21847" "Galntl6" "Gpc6" "Pdzrn4"
## [45] "Grik1" "Nos1" "Astn2" "Dgkb"
## [49] "Kcnd2" "4930555F03Rik" "Dgkg" "Car10"
## [53] "Vwc2l" "Ptprt" "Zfp804b" "Grm5"
## [57] "A330015K06Rik" "Dcc" "Sema5a" "Spock3"
## [61] "Wdr17" "Unc5d" "Chsy3" "Sst"
## [65] "Vip" "Nrg3" "Pbx3" "Csmd1"
## [69] "Fgf14" "Gpr149" "Tshz2" "Zfhx3"
## [73] "Bnc2" "Hs6st3" "Trps1" "Tafa1"
## [77] "Pde4d" "Gpc5" "Kcnh7" "Tenm2"
## [81] "Gm45321" "Lrrtm4" "Serpini1" "Cdh6"
## [85] "Nxph2" "Robo1" "Plxna4" "Il1rapl1"
## [89] "Plcl1" "4930432L08Rik" "Epha5" "Gm20754"
## [93] "Rasgef1b" "Tafa2" "Dpp10" "Clstn2"
## [97] "Gm15398" "Gabrg3" "Tac1" "5530401A14Rik"
## [101] "Fam155a" "Dach1" "4930587E11Rik" "Ccbe1"
## [105] "Gm42756" "Lrrc4c" "Auts2" "Cadm2"
## [109] "Nxph1" "Grm7" "Trhde" "Col25a1"
## [113] "Piezo2" "9530059O14Rik" "Dock10" "Cpa6"
## [117] "Alcam" "B230312C02Rik" "mt-Co3" "1700111E14Rik"
## [121] "Tnr" "Chrna9" "Kctd8" "Pde1a"
## [125] "Skap1" "4930445B16Rik" "Gm20658" "Gm28376"
## [129] "Gm20713" "Penk" "Gm15680" "Gm36041"
## [133] "Gm11342" "Gm15584" "Cntn3" "Thsd7b"
## [137] "Lrp1b" "Cadps2" "Efr3a" "Itgb6"
## [141] "Gm16638" "Cntn4" "Pcdh7" "Grin3a"
## [145] "Plcxd3" "mt-Atp6" "Ctnna3" "Kcnab1"
## [149] "Pcdh10" "E130114P18Rik" "Ndst4" "Dmd"
## [153] "Brinp2" "Cdc14a" "Fbxw15" "Gm12128"
## [157] "Gm12239" "mt-Co1" "Sorcs1" "Gm48742"
## [161] "Gm11339" "Caln1" "Fhit" "Cysltr2"
## [165] "Gm48877" "Tox" "Myl1" "Etv1"
## [169] "D030068K23Rik" "C79798" "Gm16226" "Gm2516"
## [173] "Pcdh11x" "Galnt13" "Cacna2d3" "Slc44a5"
## [177] "Galnt18" "Specc1" "Tmem132cos" "Gm20631"
## [181] "Rgs6" "Rspo2" "Fbxw24" "4930428E07Rik"
## [185] "St6galnac3" "Unc5c" "Pcdh15" "Gria1"
## [189] "Ptprd" "Synpr" "Gna14" "4930422I22Rik"
## [193] "Chrna7" "Tmem132c" "Grid2" "Gfra1"
## [197] "4930590L20Rik" "Slc4a4" "Alk" "Rtl4"
## [201] "Ghr" "Gm20642" "Gm34623" "Pde4b"
## [205] "Dock2" "Kirrel3" "Gm28153" "Pantr1"
## [209] "Rarb" "Fgf13" "C130073E24Rik" "Mdga2"
## [213] "Kctd16" "Kcnj3" "Kif26b" "Nkain2"
## [217] "Luzp2" "Tmtc2" "mt-Co2" "Epha8"
## [221] "Dgki" "Slc35f4" "Galnt17" "Pde10a"
## [225] "Gm10848" "Sybu" "Lama2" "Trpm3"
## [229] "Stxbp6" "Naaladl2" "Kcnt2" "Kitl"
## [233] "Gm4876" "Lsamp" "Gm43948" "Pard3bos1"
## [237] "Zfp536" "Gm48094" "Tenm3" "Nlgn1"
## [241] "Meis1" "Nrg3os" "Timp3" "Pld5"
## [245] "Kcnq3" "Npas3" "Prune2" "9630028H03Rik"
## [249] "Htr4" "Dsc3" "Syn3" "Fat3"
## [253] "Airn" "Cntnap5a" "1700024B18Rik" "Dlc1"
## [257] "Negr1" "Gm6994" "Frmd4b" "Popdc3"
## [261] "A830018L16Rik" "Eya1" "Cux2" "Grid1"
## [265] "Pigk" "mt-Nd2" "mt-Nd4" "Tenm4"
## [269] "Pde7b" "Adgrl3" "Rmst" "Zfp521"
## [273] "Arhgap6" "Tmem163" "8030451O07Rik" "Gm11418"
## [277] "Colec12" "Dlgap2" "Vcan" "Mid1"
## [281] "Ryr3" "Col18a1" "Pifo" "Thsd4"
## [285] "Arhgap24" "Bmpr1b" "Tenm1" "Hmcn1"
## [289] "Chrm2" "Gm13269" "Nek1" "Nell1"
## [293] "Cacnb2" "Calcb" "St18" "Ank2"
## [297] "Gm48321" "Gm49678" "Gm28154" "Nfia"DIG <- grep("^mt-|^Tra|^Trb|^Trg|^Trd|^Tcr|^Igm|^Igh|^Igk|^Igl|Jchain|Mzb1|Vpreb|^Hsp|^Rps|^Rpl|Hbb-|Hba-|^Dnaj|^Jun|^Fos|^AY|^AA|^AC|^AI|^AW|^BC|^Gm|^Hist|Lars2|Rik$|-ps",
feature.raw,value = T)
# gender-relaged genes
GRG <- c("Xist","Tsix","Uty","Ddx3y","Eif2s3y","Kdm5d")
CC_gene <- Hmisc::capitalize(tolower(as.vector(unlist(cc.genes.updated.2019))))DIG## [1] "Gm30613" "Gm32647" "Gm29521" "Gm15261"
## [5] "4930509J09Rik" "Gm21847" "4930555F03Rik" "A330015K06Rik"
## [9] "Gm45321" "4930432L08Rik" "Gm20754" "Gm15398"
## [13] "5530401A14Rik" "4930587E11Rik" "Gm42756" "9530059O14Rik"
## [17] "B230312C02Rik" "mt-Co3" "1700111E14Rik" "4930445B16Rik"
## [21] "Gm20658" "Gm28376" "Gm20713" "Gm15680"
## [25] "Gm36041" "Gm11342" "Gm15584" "Gm16638"
## [29] "mt-Atp6" "E130114P18Rik" "Gm12128" "Gm12239"
## [33] "mt-Co1" "Gm48742" "Gm11339" "Gm48877"
## [37] "D030068K23Rik" "Gm16226" "Gm2516" "Gm20631"
## [41] "4930428E07Rik" "4930422I22Rik" "4930590L20Rik" "Gm20642"
## [45] "Gm34623" "Gm28153" "C130073E24Rik" "mt-Co2"
## [49] "Gm10848" "Gm4876" "Gm43948" "Gm48094"
## [53] "9630028H03Rik" "1700024B18Rik" "Gm6994" "A830018L16Rik"
## [57] "mt-Nd2" "mt-Nd4" "8030451O07Rik" "Gm11418"
## [61] "Gm13269" "Gm48321" "Gm49678" "Gm28154"
## [65] "Gm15577" "Gm32509" "Gm42836" "Gm45486"
## [69] "Gm27153" "Gm11417" "4930473D10Rik" "Gm49003"
## [73] "Gm12296" "Gm48715" "4930486F22Rik" "Gm28905"
## [77] "Gm26995" "Gm42439" "Gm20616" "Gm16168"
## [81] "4930517O19Rik" "Gm21798" "Gm26936" "Gm11099"
## [85] "Gm13629" "4930419G24Rik" "Gm27016" "4933432K03Rik"
## [89] "Gm48749" "Gm4117" "Gm45352" "Gm47271"
## [93] "Gm37459" "Gm3848" "Gm11802" "Gm45323"
## [97] "4930447N08Rik" "D030055H07Rik" "Gm26873" "Gm15990"
## [101] "Gm45341" "Gm11844" "Gm1992" "Gm13963"
## [105] "Gm19744" "Gm26827" "Gm28410" "Gm49127"
## [109] "Gm32335" "Gm20641" "Gm49959" "2610307P16Rik"
## [113] "Gm35188" "Gm48747" "Gm11817" "Gm48727"
## [117] "Gm15336" "Gm44151" "4921534H16Rik" "Gm48093"
## [121] "Gm38604" "Gm14211" "Gm48765" "Gm16541"
## [125] "Gm16599" "Gm44071" "Gm46124" "A730090N16Rik"
## [129] "Gm12002" "Gm50368" "Gm12216" "A230004M16Rik"
## [133] "Gm37679" "Gm45645" "Gm31698" "9330175M20Rik"
## [137] "Gm36975" "Gm11713" "9630013A20Rik" "Gm16193"
## [141] "Gm16685" "4833422C13Rik" "Gm42413" "Gm28501"
## [145] "Gm15738" "Gm16833" "E330018M18Rik" "Gm13919"
## [149] "B430010I23Rik" "B230307C23Rik" "Gm26672" "Gm5441"
## [153] "Gm40841" "Gm47283" "4930444A19Rik" "4933413L06Rik"
## [157] "Gm48228" "4933424G05Rik" "Gm26954" "Gm16070"
## [161] "Gm15283" "Gm26854" "Gm31763" "4921511C10Rik"
## [165] "Gm5535" "Gm43569" "2410021H03Rik" "Gm36198"
## [169] "Gm16178" "Gm12709" "Gm19522" "Gm33696"
## [173] "4930545L23Rik" "Gm28375" "mt-Cytb" "Gm15563"
## [177] "Gm16189" "4930567K12Rik" "4930547E14Rik" "Gm41609"
## [181] "Gm9968" "mt-Nd3" "4930484I04Rik" "A230009B12Rik"
## [185] "4930570B17Rik" "Gm4258" "Gm11149" "Gm33228"
## [189] "Gm11437" "Gm49961" "5730522E02Rik" "Gm10550"
## [193] "Gm43376" "Gm36146" "Gm27151" "Gm41505"
## [197] "3110021N24Rik" "4930595D18Rik" "Gm15337" "Gm42556"
## [201] "Gm15614" "Gm11536" "4930511M06Rik" "Gm15886"
## [205] "4930447A16Rik" "Gm47027" "Gm15155" "A330008L17Rik"
## [209] "Gm34030" "Gm50270" "Gm5089" "1810062O18Rik"
## [213] "4930592A05Rik" "Gm28750" "Gm15496" "Gm48383"
## [217] "Gm47071" "Gm14798" "Gm49226" "Gm48342"
## [221] "1810034E14Rik" "Gm16183" "Gm26911" "Gm2694"
## [225] "Gm16759" "mt-Nd1" "Gm15904" "Gm34299"
## [229] "Rps29" "Gm16093" "Gm19605" "A930002C04Rik"
## [233] "Gm48512" "C330002G04Rik" "Gm39214" "mt-Nd5"
## [237] "4933408N05Rik" "Gm32014" "Gm16036" "A930037H05Rik"
## [241] "Gm49179" "Gm39090" "Gm37240" "Gm37229"
## [245] "Rpl37" "Gm10563" "Gm11457" "Rps27a"
## [249] "9630014M24Rik" "1700109K24Rik" "Gm28198" "A330102I10Rik"
## [253] "Gm2087" "Gm43000" "2610204G07Rik" "2700054A10Rik"
## [257] "4930414N06Rik" "4932443L11Rik" "4930532I03Rik" "Rpl39"
## [261] "Gm17231" "Gm19710" "4930512B01Rik" "Gm43915"
## [265] "Gm31683" "Gm29530" "Gm17178" "Gm20470"
## [269] "2810432F15Rik" "Traf3ip2" "Gm26918" "Gm17545"
## [273] "Gm42418" "Gm10135" "Gm38251" "Gm26542"
## [277] "Gm26691" "1700037C18Rik" "Gm38304" "C630031E19Rik"
## [281] "A830019P07Rik" "Gm14858" "4930527B05Rik" "Gm7580"
## [285] "Gm42477" "9330188P03Rik" "Gm12353" "Gm27241"
## [289] "Gm19461" "6530409C15Rik" "Gm13402" "Gm16958"
## [293] "Gm50013" "Gm37640" "Gm12592" "Gm50163"
## [297] "Gm10785" "1700085D07Rik" "AC154200.1" "Gm38134"
## [301] "Gm50152" "4831440D22Rik" "Gm46367" "A330032B11Rik"
## [305] "Gm17058" "Gm16066" "Gm15478" "Gm15700"
## [309] "Gm43713" "Gm49417" "1700084C06Rik" "Gm20149"
## [313] "Rps6ka3" "Gm50232" "Gm17227" "Gm42609"
## [317] "D830024N08Rik" "Gm30054" "4930557K07Rik" "Gm26759"
## [321] "Gm17036" "1600010M07Rik" "Rpl24" "Gm20536"
## [325] "Gm37571" "Rps21" "Gm48677" "Gm15489"
## [329] "Gm49521" "3110082I17Rik" "Gm5820" "Gm10544"
## [333] "Gm19667" "Gm47153" "Gm26724" "Gm26658"
## [337] "Gm39326" "Gm10629" "Gm10658" "AC163638.2"
## [341] "2610027K06Rik" "A230006K03Rik" "Rpl26" "Rpl37a"
## [345] "Gm47167" "Gm48708" "Gm50020" "1110046J04Rik"
## [349] "Gm15396" "Rpsa" "Gm16043" "Rps28"
## [353] "Gm10130" "Gm43192" "A230072E10Rik" "2610020C07Rik"
## [357] "D830036C21Rik" "4933400F21Rik" "Gm37768" "2610316D01Rik"
## [361] "Dnajc15" "B020031H02Rik" "Gm10791" "Rpl38"
## [365] "Rplp1" "6430590A07Rik" "4933433G19Rik" "mt-Nd6"
## [369] "4930583P06Rik" "Rps23" "4930455C13Rik" "Gm44686"
## [373] "Rplp2" "Hsp90ab1" "Dnajc19" "5033428I22Rik"
## [377] "Gm31323" "4930556N13Rik" "2810004N23Rik" "Gm29114"
## [381] "Rps24" "Traf3" "Rpl36" "Rpl27"
## [385] "Gm19466" "Gm47134" "Gm20404" "Gm17529"
## [389] "Gm10353" "1700126G02Rik" "2700069I18Rik" "BC052040"
## [393] "Gm15787" "Gm16794" "C130071C03Rik" "Gm15270"
## [397] "Gm26979" "Gm50250" "Gm16552" "A630089N07Rik"
## [401] "Gm48855" "Gm15952" "Gm41764" "Gm46440"
## [405] "1700110K17Rik" "Gm34868" "A530058N18Rik" "4930506C21Rik"
## [409] "Gm28085" "Rps20" "Rpl30" "Gm15243"
## [413] "Rps15a" "4921511I17Rik" "E530011L22Rik" "Gm11659"
## [417] "Gm15893" "Gm49915" "1700012D14Rik" "2210408I21Rik"
## [421] "Gm14372" "5330417C22Rik" "Rps12" "4930480K15Rik"
## [425] "Gm47350" "4930429F24Rik" "Rpl23" "Gm50138"
## [429] "0610043K17Rik" "Gm11906" "Gm26788" "Gm48239"
## [433] "AW554918" "B230216N24Rik" "Rpl32" "6530437J22Rik"
## [437] "Gm16279" "Gm28379" "Rpl35a" "Gm49189"
## [441] "Gm45509" "Gm3294" "Gm45435" "Gm47551"
## [445] "Gm13187" "9330182L06Rik" "4930565N06Rik" "Gm17494"
## [449] "1700087M22Rik" "Gm17477" "Gm26749" "Rpl27a"
## [453] "Rps9" "4930473A02Rik" "4930556J24Rik" "Gm16185"
## [457] "Rps14" "4930586N03Rik" "4930549G23Rik" "Rpl13a"
## [461] "Rps26" "Gm26981" "Rps27" "C530008M17Rik"
## [465] "Rps3a1" "Gm1604a" "Rpl8" "Rpl11"
## [469] "Gm32743" "Gm37982" "Rpl10a" "Rpl3"
## [473] "Rps6ka5" "E130307A14Rik" "AW551984" "Rps8"
## [477] "A230056P14Rik" "Gm11250" "6330403K07Rik" "Gm26531"
## [481] "Gm26673" "Rpl6" "Gm12473" "E230016M11Rik"
## [485] "Dnajc6" "Gm44649" "1700080N15Rik" "6430548M08Rik"
## [489] "Rpl5" "Gm13748" "Gmcl1" "4933421D24Rik"
## [493] "Rpl12" "Trafd1" "Gm47460" "Gm20721"
## [497] "2610035D17Rik" "Gm36992" "Gm41804" "Gm10125"
## [501] "Rpl19" "Rps17" "Gm19938" "Dnaja3"
## [505] "Gm48678" "Gm16083" "Gm49336" "Gmeb2"
## [509] "1500004A13Rik" "Rpl21" "D430019H16Rik" "Gm12940"
## [513] "B130055M24Rik" "Gm47802" "4933406I18Rik" "2700081O15Rik"
## [517] "Gm47827" "Gm30648" "Gm15632" "D430042O09Rik"
## [521] "Gm28370" "Gm32250" "Gm16549" "1110002L01Rik"
## [525] "Rps4x" "Dnajc24" "Rpl41" "2510009E07Rik"
## [529] "Rpl13" "1110059E24Rik" "Gm43388" "9530026P05Rik"
## [533] "1110019D14Rik" "mt-Atp8" "mt-Nd4l" "4931403E22Rik"
## [537] "Gm49191" "Gm49171" "Gm45600" "Gm13708"
## [541] "1700094D03Rik" "AC149090.1" "Trank1" "Rpl22l1"
## [545] "Gm26879" "Rpl34" "4930578M01Rik" "Rpl35"
## [549] "AC150683.1" "Rps18" "Gm12227" "Gm16351"
## [553] "Ndufb1-ps" "Rps3" "Gm44129" "Hsp90aa1"
## [557] "5430427M07Rik" "Rpl18" "Gm15764" "9330185C12Rik"
## [561] "Rps10" "Dnajc7" "6720427I07Rik" "Dnajc3"
## [565] "AW549877" "Rps19" "Gm29237" "Gm47889"
## [569] "Gm17202" "Gm27008" "Rps7" "Gm10076"
## [573] "Gm12298" "Gm42788" "Gm47661" "Rps13"
## [577] "4930599N23Rik" "Gm12224" "1600020E01Rik" "D630045J12Rik"
## [581] "Gm26618" "Gm20456" "Gm3227" "AI480526"
## [585] "Gm30094" "Rpl7a" "Gm11867" "Gm10974"
## [589] "Gm11465" "2610037D02Rik" "Gm11714" "Rpl18a"
## [593] "Gm16068" "Gm36279" "BC048559" "2010300C02Rik"
## [597] "2210408F21Rik" "5730480H06Rik" "3110056K07Rik" "Rpl9"
## [601] "2700049A03Rik" "Hspa5" "Hist3h2ba" "Gm45502"
## [605] "Gm36363" "Rps6" "1810026B05Rik" "Gm28294"CC_gene## [1] "Mcm5" "Pcna" "Tyms" "Fen1" "Mcm7" "Mcm4"
## [7] "Rrm1" "Ung" "Gins2" "Mcm6" "Cdca7" "Dtl"
## [13] "Prim1" "Uhrf1" "Cenpu" "Hells" "Rfc2" "Polr1b"
## [19] "Nasp" "Rad51ap1" "Gmnn" "Wdr76" "Slbp" "Ccne2"
## [25] "Ubr7" "Pold3" "Msh2" "Atad2" "Rad51" "Rrm2"
## [31] "Cdc45" "Cdc6" "Exo1" "Tipin" "Dscc1" "Blm"
## [37] "Casp8ap2" "Usp1" "Clspn" "Pola1" "Chaf1b" "Mrpl36"
## [43] "E2f8" "Hmgb2" "Cdk1" "Nusap1" "Ube2c" "Birc5"
## [49] "Tpx2" "Top2a" "Ndc80" "Cks2" "Nuf2" "Cks1b"
## [55] "Mki67" "Tmpo" "Cenpf" "Tacc3" "Pimreg" "Smc4"
## [61] "Ccnb2" "Ckap2l" "Ckap2" "Aurkb" "Bub1" "Kif11"
## [67] "Anp32e" "Tubb4b" "Gtse1" "Kif20b" "Hjurp" "Cdca3"
## [73] "Jpt1" "Cdc20" "Ttk" "Cdc25c" "Kif2c" "Rangap1"
## [79] "Ncapd2" "Dlgap5" "Cdca2" "Cdca8" "Ect2" "Kif23"
## [85] "Hmmr" "Aurka" "Psrc1" "Anln" "Lbr" "Ckap5"
## [91] "Cenpe" "Ctcf" "Nek2" "G2e3" "Gas2l3" "Cbx5"
## [97] "Cenpa"features.filt <- setdiff(feature.raw, c(DIG,CC_gene,GRG))
length(features.filt)## [1] 2386head(features.filt,300)## [1] "Cntn5" "Kcnip4" "Mgat4c" "Cntnap2" "Zfp804a"
## [6] "Klhl1" "Kcnb2" "Gal" "Nrxn3" "Pcdh9"
## [11] "Adgrg6" "Lingo2" "Asic2" "Robo2" "Ntng1"
## [16] "Rbfox1" "Nrg1" "Sgcz" "Cpne4" "Cdh18"
## [21] "Cmah" "Prkg1" "Schip1" "Tmeff2" "Ebf1"
## [26] "Ntrk3" "Kcnq5" "Cdh9" "Brinp3" "Egfem1"
## [31] "March1" "Ano2" "Cdh8" "Nmu" "Csmd3"
## [36] "Galntl6" "Gpc6" "Pdzrn4" "Grik1" "Nos1"
## [41] "Astn2" "Dgkb" "Kcnd2" "Dgkg" "Car10"
## [46] "Vwc2l" "Ptprt" "Zfp804b" "Grm5" "Dcc"
## [51] "Sema5a" "Spock3" "Wdr17" "Unc5d" "Chsy3"
## [56] "Sst" "Vip" "Nrg3" "Pbx3" "Csmd1"
## [61] "Fgf14" "Gpr149" "Tshz2" "Zfhx3" "Bnc2"
## [66] "Hs6st3" "Trps1" "Tafa1" "Pde4d" "Gpc5"
## [71] "Kcnh7" "Tenm2" "Lrrtm4" "Serpini1" "Cdh6"
## [76] "Nxph2" "Robo1" "Plxna4" "Il1rapl1" "Plcl1"
## [81] "Epha5" "Rasgef1b" "Tafa2" "Dpp10" "Clstn2"
## [86] "Gabrg3" "Tac1" "Fam155a" "Dach1" "Ccbe1"
## [91] "Lrrc4c" "Auts2" "Cadm2" "Nxph1" "Grm7"
## [96] "Trhde" "Col25a1" "Piezo2" "Dock10" "Cpa6"
## [101] "Alcam" "Tnr" "Chrna9" "Kctd8" "Pde1a"
## [106] "Skap1" "Penk" "Cntn3" "Thsd7b" "Lrp1b"
## [111] "Cadps2" "Efr3a" "Itgb6" "Cntn4" "Pcdh7"
## [116] "Grin3a" "Plcxd3" "Ctnna3" "Kcnab1" "Pcdh10"
## [121] "Ndst4" "Dmd" "Brinp2" "Cdc14a" "Fbxw15"
## [126] "Sorcs1" "Caln1" "Fhit" "Cysltr2" "Tox"
## [131] "Myl1" "Etv1" "C79798" "Pcdh11x" "Galnt13"
## [136] "Cacna2d3" "Slc44a5" "Galnt18" "Specc1" "Tmem132cos"
## [141] "Rgs6" "Rspo2" "Fbxw24" "St6galnac3" "Unc5c"
## [146] "Pcdh15" "Gria1" "Ptprd" "Synpr" "Gna14"
## [151] "Chrna7" "Tmem132c" "Grid2" "Gfra1" "Slc4a4"
## [156] "Alk" "Rtl4" "Ghr" "Pde4b" "Dock2"
## [161] "Kirrel3" "Pantr1" "Rarb" "Fgf13" "Mdga2"
## [166] "Kctd16" "Kcnj3" "Kif26b" "Nkain2" "Luzp2"
## [171] "Tmtc2" "Epha8" "Dgki" "Slc35f4" "Galnt17"
## [176] "Pde10a" "Sybu" "Lama2" "Trpm3" "Stxbp6"
## [181] "Naaladl2" "Kcnt2" "Kitl" "Lsamp" "Pard3bos1"
## [186] "Zfp536" "Tenm3" "Nlgn1" "Meis1" "Nrg3os"
## [191] "Timp3" "Pld5" "Kcnq3" "Npas3" "Prune2"
## [196] "Htr4" "Dsc3" "Syn3" "Fat3" "Airn"
## [201] "Cntnap5a" "Dlc1" "Negr1" "Frmd4b" "Popdc3"
## [206] "Eya1" "Cux2" "Grid1" "Pigk" "Tenm4"
## [211] "Pde7b" "Adgrl3" "Rmst" "Zfp521" "Arhgap6"
## [216] "Tmem163" "Colec12" "Dlgap2" "Vcan" "Mid1"
## [221] "Ryr3" "Col18a1" "Pifo" "Thsd4" "Arhgap24"
## [226] "Bmpr1b" "Tenm1" "Hmcn1" "Chrm2" "Nek1"
## [231] "Nell1" "Cacnb2" "Calcb" "St18" "Ank2"
## [236] "Nfia" "Sgcd" "Synpo2" "Khdrbs2" "Dach2"
## [241] "Sdk1" "Nell1os" "Htr2b" "Adcy2" "Syt6"
## [246] "Olfm3" "Mef2c" "Slc26a4" "Creb5" "Plcb1"
## [251] "Lrrc7" "Fbxl7" "Cdh20" "Ptprz1" "Cntnap5b"
## [256] "Arpp21" "Scg2" "Man1a" "Xylt1" "Adamtsl1"
## [261] "Zbbx" "Cdh11" "Edil3" "Mir99ahg" "Ammecr1"
## [266] "Mast4" "Arhgap15" "Rims1" "Eda" "Oprk1"
## [271] "Inpp4b" "Tcf7l2" "Hs3st2" "Nfib" "Ngfr"
## [276] "Flrt2" "Cpne8" "Samd4" "Kcnk2" "Grm3"
## [281] "Unc13c" "Cdh10" "Gda" "Ano5" "Plpp4"
## [286] "Tafa5" "Col5a2" "Gabrb1" "Prkca" "Frmpd4"
## [291] "Nxn" "Gucy1a2" "Usp6nl" "Itgbl1" "Bcl2"
## [296] "Hccs" "Igf2r" "Thsd7a" "Casz1" "Ntrk2"length(feature.raw)## [1] 3000length(features.filt)## [1] 2386GRG %in% feature.raw## [1] FALSE FALSE FALSE FALSE FALSE FALSEGRG %in% features.filt## [1] FALSE FALSE FALSE FALSE FALSE FALSEanchors
all.anchors <- FindIntegrationAnchors(object.list = batchcor.seu.list,
dims = 1:50,
anchor.features = features.filt)## Scaling features for provided objects## Finding all pairwise anchors## Running CCA## Merging objects## Finding neighborhoods## Finding anchors## Found 37773 anchors## Filtering anchors## Retained 9109 anchorsall.anchors## An AnchorSet object containing 18218 anchors between 2 Seurat objects
## This can be used as input to IntegrateData.Integration
all.anchors <- IntegrateData(anchorset = all.anchors, dims = 1:50)## Merging dataset 1 into 2## Extracting anchors for merged samples## Finding integration vectors## Finding integration vector weights## Integrating dataall.anchors## An object of class Seurat
## 47749 features across 28452 samples within 3 assays
## Active assay: integrated (2386 features, 2386 variable features)
## 2 other assays present: RNA, SCTall.anchors@assays$SCT@SCTModel.list## $model1
## An sctransform model.
## Model formula: y ~ log_umi
## Parameters stored for 19662 features, 11932 cells.
##
## $model1.1
## An sctransform model.
## Model formula: y ~ log_umi
## Parameters stored for 20759 features, 16520 cells.#rm(batchcor.seu.list)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 8167903 436.3 13105874 700.0 13105874 700.0
## Vcells 1089076968 8309.0 2248933876 17158.1 2808264459 21425.4#head(all.anchors$orig.ident)
#unique(all.anchors$orig.ident)unique(all.anchors$cnt)## [1] "Stst.CTL" "Stst.CKO" "CR7d.CTL" "CR7d.CKO"#
all.anchors$orig.ident <- factor(all.anchors$orig.ident,
levels = c("Stst.CTL_CKO","CR7d.CTL_CKO"))
# #table(all.anchors@meta.data[,c("cnt","condition")])table(all.anchors@meta.data[,c("cnt","orig.ident")])## orig.ident
## cnt Stst.CTL_CKO CR7d.CTL_CKO
## CR7d.CKO 0 6240
## CR7d.CTL 0 10280
## Stst.CKO 6023 0
## Stst.CTL 5909 0VlnPlot(all.anchors, features = GRG, group.by = "sample", assay = "RNA")
all males
clustering
all.anchors <- ScaleData(object = all.anchors, verbose = TRUE,
vars.to.regress = c("percent.mt","percent.rb","nCount_RNA"))## Regressing out percent.mt, percent.rb, nCount_RNA## Centering and scaling data matrix# sample condition to regress
# cnt ?
# orig.idnet ?
# condition ?
# sex ?gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 8290322 442.8 13105874 700.0 13105874 700.0
## Vcells 1157659183 8832.3 2248933876 17158.1 2808264459 21425.4length(all.anchors@assays$integrated@var.features)## [1] 2386all.anchors <- RunPCA(all.anchors, do.print = TRUE,
features = all.anchors@assays$integrated@var.features,
seed.use = 133,
npcs = 100,
#ndims.print = 1,
verbose = T)## PC_ 1
## Positive: Nrg3, Grid2, Lrrtm4, Pcdh15, Trpm3, Ndst4, Cadm1, Kcnq3, Frmpd4, Epha5
## Cacnb2, Slit2, Kcnc2, Nrg3os, Kcnd2, Nkain2, Fstl5, Syn3, Magi1, Galntl6
## Ctnna3, Nav3, Lrrc4c, Kcnab1, Plce1, Kcnma1, Nos1, Pde4b, Lrp1b, Dmd
## Negative: Ntrk3, Ano2, Robo2, Cdh8, Nrxn3, Tmeff2, Cpne4, Clstn2, Myl1, Plxna4
## Pdzrn4, Adgrg6, Mgat4c, Zfp804a, Gpr149, Pcdh10, Ccbe1, Cysltr2, Cdh6, Dgkg
## Cux2, Spock3, Astn2, Grin3a, Iqgap2, Cntn5, Itgb6, Arhgap6, Kcnb2, Pcdh9
## PC_ 2
## Positive: Bnc2, Rbfox1, Ptprt, Gpc6, Tshz2, Grik1, Tafa1, Mdga2, Tox, St6galnac3
## Adgrb3, Frmd4b, Pcdh7, Brinp2, Fbxw15, Cdc14a, Plcxd3, Agbl4, Negr1, Pde4b
## Pld5, Oprk1, Fbxw24, Dlgap2, Specc1, Chat, Xylt1, Unc5c, Dock2, Adamtsl1
## Negative: Auts2, Nos1, Dgkb, Alcam, Etv1, Fam155a, Cadps2, Gfra1, Kcnq5, Egfem1
## Kcnt2, Asic2, Kcnab1, Dach1, Schip1, Plekha5, Rgs6, Ank2, Cmah, Epha5
## Cntnap5a, Nav3, Stxbp6, Hs6st3, Tmem108, Creb5, Ncam2, Il1rapl1, Ablim2, Ebf1
## PC_ 3
## Positive: Kcnip4, Cdh18, Csmd3, Klhl1, Kctd8, Pbx3, Cadm2, Gabrg3, Htr4, Cntn3
## Dlc1, Pde4d, Meis1, March1, Serpini1, C79798, Sema5a, Skap1, Car10, Khdrbs2
## Prkg1, Edil3, Zfhx3, Vwc2l, Plcl1, Gpc6, Dmd, Cdh9, Nrp2, Pakap
## Negative: Sgcd, Ptprg, Adgrg6, Nfia, Fgf13, Filip1, Nfib, Cysltr2, Sema6d, Gpr149
## Ccbe1, Malat1, Slc4a4, Nos1, Nmu, Cntnap5a, Grin3a, Ano2, Dapk2, Tshz2
## Airn, Dgkg, Zfp804a, Cbln2, Itgb6, Efr3a, Cpne4, Zfp521, Ryr2, Zfp536
## PC_ 4
## Positive: Lingo2, Dock10, Prkg1, Kcnt2, Ndst4, Fgf13, Epha5, Kcnip4, Ctnna3, Gda
## Dmd, Lrrtm4, Tac1, Cntn5, Lrrc4c, Nxph1, Sorcs1, Chl1, Kctd8, Thsd4
## Ptprz1, Grem2, Ank2, Lama2, Hs3st2, Rgs6, Hgf, Rora, Lrrc7, Galntl6
## Negative: Chsy3, Trhde, March1, Ebf1, Trps1, Cntn4, Gal, Enox1, Nrg1, Trpm3
## Col18a1, Ntng1, Zmat4, Sdk1, Cpa6, Dcc, Ccser1, Tenm4, Shisa6, Plcxd3
## Csmd1, Npas3, Nkain3, Kcnh7, Sctr, Prune2, Tenm1, Sez6l, Sema5a, Galnt17
## PC_ 5
## Positive: Nrg1, Trhde, Ptprd, Cntn4, Lsamp, Egfem1, Rmst, Cntn3, Adgrl2, Kcnd2
## Sgcz, Csmd3, Gal, Cntn5, Luzp2, Trps1, Ebf1, Nav2, Cpa6, Zmat4
## Hs6st3, Csmd1, Asic2, Astn2, Moxd1, Col18a1, Nkain3, Lrp1b, Rasgrf2, Kcnip4
## Negative: Dgkb, Klhl1, Vwc2l, Rasgef1b, Pbx3, Il1rapl1, Alk, Cdh9, Mgat4c, Alcam
## Zfhx3, Dpp10, Vcan, Sema5a, Fam155a, Galnt18, Auts2, Galnt13, Zbbx, Olfr78
## Thsd7b, C79798, Pcdh7, Scgn, Nek1, Serpini1, P3h2, Lncbate1, Stard13, Gucy1a2DimHeatmap(all.anchors, dims = 1:12, cells = 1500, balanced = TRUE,ncol = 4)
ElbowPlot(all.anchors, ndims = 100)
ElbowPlot(all.anchors, ndims = 50)
Integrated
all.anchors@meta.data[,grep("snn|pANN|cluster|sort|preAnno|snn|Doublet",colnames(all.anchors@meta.data))] <- NULL
head(all.anchors@meta.data)## orig.ident nCount_RNA nFeature_RNA percent.mt percent.rb
## AAACCCAAGAATACAC-1_2 Stst.CTL_CKO 2282 1290 0.13146363 0.1314636
## AAACCCAAGCAATAGT-1_2 Stst.CTL_CKO 1535 1011 0.13029316 0.5863192
## AAACCCAAGGTGAGCT-1_2 Stst.CTL_CKO 1049 737 0.00000000 0.4766444
## AAACCCAAGTGGACGT-1_2 Stst.CTL_CKO 4119 1907 0.09711095 0.4855547
## AAACCCACAACGAGGT-1_2 Stst.CTL_CKO 1591 1046 0.69138906 0.4399749
## AAACCCACAAGAGTAT-1_2 Stst.CTL_CKO 2106 1250 0.28490028 0.2849003
## FB.info S.Score G2M.Score Phase cnt Anno1
## AAACCCAAGAATACAC-1_2 CTL.3 -0.00110357 0.012058898 G2M Stst.CTL EMN1
## AAACCCAAGCAATAGT-1_2 CKO.3 0.02907907 -0.015558699 S Stst.CKO IMN1
## AAACCCAAGGTGAGCT-1_2 CTL.1 -0.01100811 -0.008015087 G1 Stst.CTL EMN1
## AAACCCAAGTGGACGT-1_2 CKO.3 0.01407052 -0.028388278 S Stst.CKO IPAN1
## AAACCCACAACGAGGT-1_2 CTL.1 -0.02143685 0.005086498 G2M Stst.CTL EMN1
## AAACCCACAAGAGTAT-1_2 CKO.3 0.02965845 -0.009057774 S Stst.CKO IMN3
## Anno2 rep Anno3 sample tissue nCount_SCT
## AAACCCAAGAATACAC-1_2 EMN1 rep3 <NA> Stst.CTL3 Ileum 1835
## AAACCCAAGCAATAGT-1_2 IMN1 rep3 <NA> Stst.CKO3 Ileum 1504
## AAACCCAAGGTGAGCT-1_2 EMN1 rep1 <NA> Stst.CTL1 Ileum 1159
## AAACCCAAGTGGACGT-1_2 IPAN1.1 rep3 <NA> Stst.CKO3 Ileum 1898
## AAACCCACAACGAGGT-1_2 EMN1 rep1 <NA> Stst.CTL1 Ileum 1550
## AAACCCACAAGAGTAT-1_2 IMN3 rep3 <NA> Stst.CKO3 Ileum 1799
## nFeature_SCT
## AAACCCAAGAATACAC-1_2 1263
## AAACCCAAGCAATAGT-1_2 989
## AAACCCAAGGTGAGCT-1_2 726
## AAACCCAAGTGGACGT-1_2 1223
## AAACCCACAACGAGGT-1_2 1027
## AAACCCACAAGAGTAT-1_2 1219DefaultAssay(all.anchors) <- "integrated"
PCsct <- 1:21all.anchors <- FindNeighbors(all.anchors, k.param = 20, dims = PCsct, compute.SNN = T, reduction = 'pca', verbose = T)## Computing nearest neighbor graph## Computing SNNall.anchors <- FindClusters(all.anchors, dims.use = PCsct, algorithm = 1, save.SNN =T, resolution =2, reduction = 'pca', verbose = T)## Warning: The following arguments are not used: dims.use, save.SNN, reduction## Suggested parameter: dims instead of dims.use## Warning: The following arguments are not used: dims.use, save.SNN, reduction## Suggested parameter: dims instead of dims.use## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 28452
## Number of edges: 1093172
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8385
## Number of communities: 32
## Elapsed time: 5 secondsall.anchors <- RunTSNE(object = all.anchors, assay = "integrated", seed.use = 233, dims = PCsct, complexity=100)
all.anchors <- RunUMAP(object = all.anchors, assay = "integrated", seed.use = 888, dims = PCsct, n.neighbors = 20, min.dist = 0.3)## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 16:27:40 UMAP embedding parameters a = 0.9922 b = 1.112## 16:27:40 Read 28452 rows and found 21 numeric columns## 16:27:40 Using Annoy for neighbor search, n_neighbors = 20## 16:27:40 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 16:27:44 Writing NN index file to temp file C:\Users\Shaorui\AppData\Local\Temp\RtmpCcsu6f\file760018e64cd4
## 16:27:44 Searching Annoy index using 1 thread, search_k = 2000
## 16:27:50 Annoy recall = 100%
## 16:27:51 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 20
## 16:27:53 Initializing from normalized Laplacian + noise (using irlba)
## 16:27:56 Commencing optimization for 200 epochs, with 838110 positive edges
## 16:28:24 Optimization finishedDimPlot(all.anchors, label = T, pt.size = 0.05, repel = F, reduction = 'tsne', group.by = "seurat_clusters") +
DimPlot(all.anchors, label = T, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "seurat_clusters")
DimPlot(all.anchors, label = T, pt.size = 0.05, repel = F, reduction = 'tsne', group.by = "Anno1") +
DimPlot(all.anchors, label = T, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "Anno1")
DimPlot(all.anchors, label = T, pt.size = 0.05, repel = F, reduction = 'tsne', group.by = "Anno2") +
DimPlot(all.anchors, label = T, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "Anno2")
color.cnt <- scales::hue_pal()(4)[c(2,1,3,4)]
color.cnt## [1] "#7CAE00" "#F8766D" "#00BFC4" "#C77CFF"all.anchors$cnt <- factor(as.character(all.anchors$cnt),
levels = c("Stst.CTL","Stst.CKO",
"CR7d.CTL","CR7d.CKO"))DimPlot(all.anchors, label = F, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "cnt", split.by = "cnt", ncol = 4, cols = color.cnt)
DimPlot(all.anchors, label = T, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "Anno1") +
DimPlot(all.anchors, label = F, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "cnt",cols = color.cnt)
DimPlot(subset(all.anchors, subset = cnt %in% c("Stst.CTL","Stst.CKO")), label = F, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "cnt", cols = color.cnt[1:2]) +
DimPlot(subset(all.anchors, subset = cnt %in% c("CR7d.CTL","CR7d.CKO")), label = F, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "cnt", cols = color.cnt[3:4])
FeaturePlot(all.anchors,
reduction = "umap", features = c("nFeature_RNA","nCount_RNA","percent.mt","percent.rb"))
FeaturePlot(subset(all.anchors, subset = cnt %in% c("Stst.CTL","Stst.CKO")),
reduction = "umap", features = c("nFeature_RNA","nCount_RNA","percent.mt","percent.rb"))
FeaturePlot(subset(all.anchors, subset = cnt %in% c("CR7d.CTL","CR7d.CKO")),
reduction = "umap", features = c("nFeature_RNA","nCount_RNA","percent.mt","percent.rb"))
all.anchors$sort_clusters <- factor(as.character(all.anchors$seurat_clusters),
levels = c(4,6,0,2,31,14,19,13,7,8,17,
5,3,9,1,18,28,11,26,
10,22,21, 25,
12,27,30,15, 16,24,23, 29, 20))VlnPlot(subset(all.anchors, subset = cnt %in% c("Stst.CTL","Stst.CKO")),
features = c("nFeature_RNA", "nCount_RNA", "percent.mt","percent.rb"), ncol = 2, pt.size = 0.01, group.by = "Anno1")
VlnPlot(subset(all.anchors, subset = cnt %in% c("CR7d.CTL","CR7d.CKO")),
features = c("nFeature_RNA", "nCount_RNA", "percent.mt","percent.rb"), ncol = 2, pt.size = 0.01, group.by = "Anno1")
VlnPlot(subset(all.anchors, subset = cnt %in% c("Stst.CTL","Stst.CKO")),
features = c("nFeature_RNA", "nCount_RNA", "percent.mt","percent.rb"), ncol = 2, pt.size = 0.01, group.by = "sort_clusters")
VlnPlot(subset(all.anchors, subset = cnt %in% c("CR7d.CTL","CR7d.CKO")),
features = c("nFeature_RNA", "nCount_RNA", "percent.mt","percent.rb"), ncol = 2, pt.size = 0.01, group.by = "sort_clusters")
might need to exclude mix-like C27/30 and low-quality like C31
check markers
DefaultAssay(all.anchors) <- "SCT"
DefaultAssay(all.anchors)## [1] "SCT"# find markers for every cluster compared to all remaining cells, with 'onlypos=F'
Idents(all.anchors) <- "sort_clusters"
all.anchors <- PrepSCTFindMarkers(all.anchors, assay = "SCT")options (scipen = 2)GEX.markers.pre <- FindAllMarkers(all.anchors, only.pos = FALSE, min.pct = 0.05,
assay = "SCT",
test.use = "MAST",
logfc.threshold = 0.25)
#GEX.markers.pre <- read.table("sn10x_integration.markers.SCT_sort.check_pre.csv", header = TRUE, sep = ",")
GEX.markers.pre %>% group_by(cluster) %>% top_n(n = 8, wt = avg_log2FC)## # A tibble: 256 x 7
## # Groups: cluster [32]
## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 0 1.44 0.998 0.419 0 4 Ptprt
## 2 0 1.31 0.981 0.377 0 4 Bnc2
## 3 0 1.21 0.994 0.514 0 4 Tshz2
## 4 0 1.19 1 0.683 0 4 Rbfox1
## 5 0 1.09 1 0.908 0 4 Nrg3
## 6 0 1.06 0.998 0.792 0 4 Negr1
## 7 0 0.944 0.938 0.483 0 4 Tox
## 8 0 0.880 0.759 0.216 0 4 Cdc14a
## 9 0 1.30 0.983 0.577 0 6 Galntl6
## 10 0 1.30 0.949 0.406 0 6 Grik1
## # ... with 246 more rowstopmarkers
markers.pre_t60 <- (GEX.markers.pre %>% group_by(cluster) %>%
filter(pct.1>0.05 & gene %in% grep("Rps|Rpl|mt-|Gm",GEX.markers.pre$gene,invert = T,value = T)) %>%
top_n(n = 60, wt = avg_log2FC) %>%
filter(p_val_adj < 0.01) %>%
ungroup() %>%
arrange(desc(avg_log2FC*pct.1),gene) %>%
distinct(gene, .keep_all = TRUE) %>%
arrange(cluster,p_val_adj))$gene
markers.pre_t120 <- (GEX.markers.pre %>% group_by(cluster) %>%
filter(pct.1>0.05 & gene %in% grep("Rps|Rpl|mt-|Gm",GEX.markers.pre$gene,invert = T,value = T)) %>%
top_n(n = 120, wt = avg_log2FC) %>%
filter(p_val_adj < 0.01) %>%
ungroup() %>%
arrange(desc(avg_log2FC*pct.1),gene) %>%
distinct(gene, .keep_all = TRUE) %>%
arrange(cluster,p_val_adj))$genettt = 889
ttt/60## [1] 14.81667ttt/64## [1] 13.89062ttt/65## [1] 13.67692pp.t120 <- list()
for(i in 1:15){
pp.t120[[i]] <- DotPlot(all.anchors, features = rev(markers.pre_t120[(60*i-59):(60*i)]), group.by = "sort_clusters") + coord_flip() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 9.6))
}
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## [[15]]
(DimPlot(subset(all.anchors,subset=cnt %in% c("Stst.CTL","Stst.CKO")), label = T, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "Anno1")+labs(title="Stst only")) +
(DimPlot(subset(all.anchors,subset=cnt %in% c("CR7d.CTL","CR7d.CKO")), label = T, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "Anno1")+labs(title="CR7d only"))
(DimPlot(subset(all.anchors,subset=cnt %in% c("Stst.CTL","Stst.CKO")), label = T, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "seurat_clusters")+labs(title="Stst only")) +
(DimPlot(subset(all.anchors,subset=cnt %in% c("CR7d.CTL","CR7d.CKO")), label = T, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "seurat_clusters")+labs(title="CR7d only"))
(DimPlot(subset(all.anchors,subset=cnt %in% c("Stst.CTL","Stst.CKO")), label = T, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "Anno1")+labs(title="Stst only")) +
(DimPlot(subset(all.anchors,subset=cnt %in% c("Stst.CTL","Stst.CKO")), label = T, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "seurat_clusters")+labs(title="Stst only"))
(DimPlot(subset(all.anchors,subset=cnt %in% c("CR7d.CTL","CR7d.CKO")), label = T, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "Anno1")+labs(title="CR7d only")) +
(DimPlot(subset(all.anchors,subset=cnt %in% c("CR7d.CTL","CR7d.CKO")), label = T, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "seurat_clusters")+labs(title="CR7d only"))